Ultra-low Power Embedded Unsupervised Learning Smart Sensor for Industrial Fault Classificatio

In this paper, an ultra-low power embedded unsupervised learning smart vibration sensor is proposed for automatic industrial monitoring and fault detection. Using K-means algorithm, it is able to detect abnormal vibrations patterns. Architecture of the system is first presented, then embedded processing algorithms composed of feature extraction and k-means algorithm are detailed. Finally, results on a vibrations simulator machine are described. They show that faults can be detected with a classification accuracy of 82% using less than 0.15% of average embedded processor resources on a ARM M4F with an average consumption of 80μW. This smart sensor is relevant for Industrial Internet Of Things (IoT) autonomous monitoring applications, having more than one year of battery life using a single CR2032 coin cell.